Hi Bioconductorians,
 
For my experience , i need to conclude about 2 effects combined = one mutation + one traitement.
 
 
In the first time, i process my analysis with ANOVA 2 way in TMEV, and I 'm interesting by gene differentially expressed dependent interaction between the two effect and a cut off = 0.001 (pvalues interaction)...it's fun!!! 
 
In the second time:  I processe the same analysis in limma  and I use this matrice 
 
             crtl  muta  trait muta_trait
 mut_crtl1    1    1       0         0
 mut_crtl2    1    1       0         0
 mut_crtl3    1    1       0         0
 mut_trait1   1    1       1         1
 mut_trait2   1    1       1         1
 mut_trait3   1    1       1         1
 wt_crtl1     1    0       0         0
 wt_crtl2     1    0       0         0
 wt_crtl3     1    0       0         0
 wt_trait1    1    0       1         0
 wt_trait2    1    0       1         0
 wt_trait3    1    0       1         0
hence , i don't use a contrast matrix.
 
After,
> fit = lmFit (eset, design)
> fit = eBayes (fit)
then topTable , where I isolate the 400 first gene.....and surprise :
 
the Probabilité  that the gene soit really expressed ( derived of B ---> P = EXP (B)/(1+EXP(B)))
is 0.82  ( not terrible as detection!!) and the lowest Pvalue in my list is 0.28 ( beeeeurrrrrk!!!!) 
Impossible to apply a cut of to select a sublist of gene
 
The top ten
IDMAtP,ValueB   probability
206153_at1,48357294,8405056,7756720,2801796-0,89217630,81952603
205978_at1,13151823,4677666,6995130,2801796-0,92110690,79615633
1568878_at0,70888173,3626835,9896730,5520668-1,2195250,59074087
208490_x_at-0,67790737,204779-5,8517720,5520668-1,28398870,553861
230323_s_at-1,13104995,409273-5,7530880,5520668-1,33150980,52815651
223196_s_at0,91246536,5386435,6297440,5700473-1,39257760,49686823
236129_at-0,84478925,61194-5,4661880,5700473-1,47649220,4568752
226106_at-0,70715488,830046-5,3943770,5700473-1,51441710,4398727
230854_at-1,06414793,967998-5,2670850,5700473-1,58329820,41059374
227750_at-0,79419665,294034-5,2281820,5700473-1,60477740,40186856

 
 
Finally , I find this result without significant statistical argument  althought I thought to get the same result ....because I thought that anova 2 way and Factors effect model  were the same technique.
 
 
Hence, 1.I haven't a good matrix... ( but i don't think it )
or /and 2. I don't nothing understand about Factors effect model  and Anova 2 way
or/and  3 .it's wrong to think that the two technique used the same technique..
 
One presicion, : with anova 2way ,I select 66 gene ( have a pv <0.001) and I  refind 58  in my second list ( composed of 400 probesets sorting by B in limma )
 
My question: 
why are there a lot of difference between the two technique?
Why  haven't I got a significant statistic with limma , whereas i get a significant result in TMEV
 
I hope that you 'll have commentaries
 
Thanks 
 
 
Greg 
Montreal centre de recherche du CHUM
Bioinformaticien
 
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